{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Random Graphs Using NetworkX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# wrap a few graph generation functions so they have the same signature\n",
    "\n",
    "def random_lobster(n, m, k, p):\n",
    "    return nx.random_lobster(n, p, p / m)\n",
    "\n",
    "def powerlaw_cluster(n, m, k, p):\n",
    "    return nx.powerlaw_cluster_graph(n, m, p)\n",
    "\n",
    "def erdos_renyi(n, m, k, p):\n",
    "    return nx.erdos_renyi_graph(n, p)\n",
    "\n",
    "def newman_watts_strogatz(n, m, k, p):\n",
    "    return nx.newman_watts_strogatz_graph(n, k, p)\n",
    "\n",
    "def plot_random_graph(n, m, k, p, generator):\n",
    "    g = generator(n, m, k, p)\n",
    "    nx.draw(g)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WrVsxbtw4bNu2zUTRMltj6uOEu7s7hvv7Y62BLUW5w5fhOBFbAB4kYdl0Oh0W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\nkNC/PCw5nB1qwDwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8882a18eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
    "        generator={'lobster': random_lobster,\n",
    "                   'power law': powerlaw_cluster,\n",
    "                   'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
    "                   u'Erdős-Rényi': erdos_renyi,\n",
    "                   });"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3+"
  },
  "widgets": {
   "state": {
    "33a1604b547041af82a9e8c2a26f0fc5": {
     "views": []
    },
    "38bb12acf658466098aa6333d226f830": {
     "views": []
    },
    "49e0b652b187465fa4fb8bd6ac4f1cea": {
     "views": []
    },
    "81467be9de88446da7dbf9b2b63d26b6": {
     "views": []
    },
    "81b22e5b30e749e68ec5163a7123c7c0": {
     "views": []
    },
    "9a4433b027bd4d309e3d474cf5700d32": {
     "views": []
    },
    "a9cee0861cd342168ea5597eeb4a8187": {
     "views": []
    },
    "ae3b8067b44445dd9175ba1370842c3e": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 5,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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WrVsxbtw4bNu2zUTRMltj6uOEu7s7hvv7Y62BLUW5w5fhOBFbAB4kYdl0Oh0W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\nnj0FiojZEr4tLY7wyEjMTE6Gr1yOBRJ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           "text/plain": "<matplotlib.figure.Figure at 0x7f8882a18eb8>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n        generator={'lobster': random_lobster,\n                   'power law': powerlaw_cluster,\n                   'Newman-Watts-Strogatz': newman_watts_strogatz,\n                   u'Erdős-Rényi': erdos_renyi,\n                   });"
       },
       "cell_index": 6,
       "root": true
      }
     ]
    },
    "bced283ed8c44ca3aa8932aee79eaf08": {
     "views": []
    },
    "c601737913c249d180aca64e3f030d63": {
     "views": []
    },
    "c8c8138feb7a40eaa9a2368f28b2ff4a": {
     "views": []
    },
    "e361e14b138841adbed8c0e01c80bcba": {
     "views": []
    }
   },
   "version": "0.0.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
